Biology subjects

Lehtinen, J.

Publications and source records attributed to Lehtinen, J..

4 recordsLinked to original sources

bioRxiv2026

Cell painting with amphiphile-protein conjugate vaccines drives B cell activation and germinal center priming to enhance humoral immunity

Most licensed vaccines against infectious diseases mediate protection by priming B cells and humoral immune responses, where efficacy largely relies on activation of germinal centers (GCs) in lymphoid tissues that serve as the training ground for a high-affinity antibody response. While subunit vaccines offer safety advantages, they tend to be poorly immunogenic with weak and short-lived antibody responses, presenting a need for engineering strategies that enhance immunogenicity. Amphiphile-protein vaccines, consisting of protein antigens modified with an albumin-binding lipid tail via a polyethylene glycol (PEG) linker, hitchhike on albumin following subcutaneous injection to enhance lymphatic trafficking and antigen-specific immune activation. Amph-vaccines also demonstrate an ability to insert their lipid tail into cell membranes, effectively 'painting' cells with multivalent antigen. We hypothesized that cell painting may play a role in driving humoral immunity by sustaining antigen persistence in lymph nodes and generating multivalent antigen presentation for B cell activation. Here, we investigated how amph-vaccine molecular properties (antigen MW and PEG linker length) influenced conformational behaviors (micelle formation, albumin hitchhiking, and membrane insertion) along with the resulting humoral immune response. Using HIV env proteins as model antigens (eOD monomer and MD39 trimer), we investigated cell membrane insertion and B cell activation via calcium flux assay in vitro, followed by lymphatic trafficking and immunogenicity studies in vivo. Our results demonstrate that amphiphile conjugation is an effective strategy for enhancing humoral immunogenicity of both monomer and trimer protein antigens, where cell painting plays a significant role in driving B cell, GC, and humoral immune activation.

bioengineering↗

bioRxiv2024

Solu - a Cloud Platform for Real-Time Genomic Pathogen Surveillance

BackgroundGenomic surveillance is extensively used for tracking public health outbreaks and healthcare-associated pathogens. Despite advancements in bioinformatics pipelines, there are still significant challenges in terms of infrastructure, expertise, and security when it comes to continuous surveillance. The existing pipelines often require the user to set up and manage their own infrastructure and are not designed for continuous surveillance that demands integration of new and regularly generated sequencing data with previous analyses. Additionally, academic projects often do not meet the privacy requirements of healthcare providers. ResultsWe present Solu, a cloud-based platform that integrates genomic data into a real-time, privacy-focused surveillance system. EvaluationSolus accuracy for taxonomy assignment, antimicrobial resistance genes, and phylogenetics, was comparable to established pathogen surveillance pipelines. In some cases, Solu identified antimicrobial resistance genes that were previously undetected. Together, these findings demonstrate the efficacy of our platform. ConclusionsBy enabling reliable, user-friendly, and privacy-focused genomic surveillance, Solu has the potential to bridge the gap between cutting-edge research and practical, widespread application in healthcare settings. The platform is available for free academic use at platform.solugenomics.com.

bioinformatics↗

bioRxiv2024

Forecasting EEG time series with WaveNet

Forecasting electroencephalography (EEG) signals, i.e., estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain-computer interfaces and closed-loop brain stimulation. As these applications are becoming more and more common, the importance of a good prediction model has increased. Previously, the autoregressive model (AR) has been employed for this task -- however, its prediction accuracy tends to fade quickly as multiple steps are predicted. We aim to improve on this by applying probabilistic deep learning to make robust longer-range forecasts. For this, we applied the probabilistic deep neural network model WaveNet to forecast resting-state EEG in theta- (4-7.5 Hz) and alpha-frequency (8-13 Hz) bands and compared it to the AR model. WaveNet reliably predicted EEG signals in both theta and alpha frequencies over 100 ms ahead, with mean errors of 0.8{+/-}0.6 {micro}V (theta) and 0.7{+/-}0.5 {micro}V (alpha), and outperformed the AR model in estimating the signal amplitude and phase. Furthermore, we found that the probabilistic approach offers a way of forecasting even more accurately while effectively discarding uncertain predictions. We demonstrate for the first time that probabilistic deep learning can be utilised to forecast resting-state EEG time series. In the future, the developed model can enhance the real-time estimation of brain states in brain-computer interfaces and brain stimulation protocols. It may also be useful for answering neuroscientific questions and for diagnostic purposes.

neuroscience↗

bioRxiv2018

HSV-1 strains circulating in Finland demonstrate uncoupling of geographic and phenotypic variation

A majority of adults in Finland are seropositive carriers of herpes simplex viruses (HSV). Infection occurs at epithelial or mucosal surfaces, after which virions enter innervating nerve endings, eventually establishing lifelong infection in neurons of the sensory or autonomic nervous system. Recent data have highlighted the genetic diversity of HSV-1 strains, and demonstrated apparent geographic patterns in strain similarity. Though multiple HSV-1 genomes have been sequenced from Europe to date, there is a lack of sequenced genomes from Nordic countries. Finlands history includes at least two major waves of human migration, suggesting the potential for diverse viruses to persist in the population. Here we used HSV-1 clinical isolates from Finland to test the relationship between viral phylogeny, genetic variation, and phenotypic characteristics. We found that Finnish HSV-1 isolates separated into two distinct phylogenetic groups, potentially reflecting historical waves of human (and viral) migration into Finland. Each HSV-1 isolate harbored a distinct set of phenotypes in cell culture, including differences in virus production, extracellular virus release, and cell-type-specific fitness. Importantly, the phylogenetic clusters were not predictive of any detectable pattern in phenotypic differences, demonstrating that whole-genome relatedness is not a proxy for overall viral phenotype. Instead, we highlight specific gene-level differences that may contribute to observed phenotypic differences, and we note that strains from different phylogenetic groups contain the same genetic variations.\n\nImportanceHerpes simplex viruses (HSV) infect a majority of adults. Recent data have highlighted the genetic diversity of HSV-1 strains, and demonstrated apparent genomic relatedness between strains from the same geographic region. We use HSV-1 clinical isolates from Finland to test the relationship between viral genomic and geographic relationships, differences in specific genes, and characteristics of viral infection. We found that viral isolates from Finland separated into two distinct groups of genomic and geographic relatedness, potentially reflecting historical patterns of human and viral migration into Finland. These Finland HSV-1 isolates had distinct infection characteristics in multiple cell types tested, which were specific to each isolate and did not group according to genomic and geographic relatedness. This demonstrates that HSV-1 strain differences in specific characteristics of infection are set by a combination of host cell type and specific viral gene-level differences.

microbiology↗